Speech Synthesis of Chinese Braille with Limited Training Data

Abstract

This paper describes to our knowledge the first Chinese Braille speech synthesis system. The system consists of modules of Braille front-end processing, prosody prediction, and speech synthesis. The Braille front-end processing includes conversion from the common Braille to Pinyin, and a high-precision Chinese character prediction model. To achieve high precision prosody prediction under limited corpus conditions, we propose a prosody prediction model based on the RoBERTa pre-trained model, which achieves an accuracy of 94.42%. Finally, a real-time TTS system based on Tacotron2 and LPCNet is proposed. We modify Tacotron2, including introducing a forward attention mechanism and extending the autoregressive correlation step size to obtain more natural speech.

Deep Learning Strategy for Braille Character Recognition

“…They claimed a high accuracy of their proposed method. A Chinese character recognition model is proposed in [30] that obtained an accuracy of 94.42%. The proposed method in [30] is extended to read the characters in natural speech.…”

“…A Chinese character recognition model is proposed in [30] that obtained an accuracy of 94.42%. The proposed method in [30] is extended to read the characters in natural speech. A combination of conventional sequence mapping method and deep learning method has been used in [31] to convert Braille characters into Hindi language, where output was generated as a speech.…”